test_config.py 13.8 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
from dataclasses import MISSING, Field, asdict, dataclass, field
5

6
7
import pytest

8
from vllm.compilation.backends import VllmBackend
9
from vllm.config import ModelConfig, PoolerConfig, VllmConfig, update_config
10
from vllm.config.load import LoadConfig
11
from vllm.config.utils import get_field
12
13
from vllm.model_executor.layers.pooler import PoolingType
from vllm.platforms import current_platform
14

15

16
17
18
19
20
21
22
23
24
25
26
27
def test_compile_config_repr_succeeds():
    # setup: VllmBackend mutates the config object
    config = VllmConfig()
    backend = VllmBackend(config)
    backend.configure_post_pass()

    # test that repr(config) succeeds
    val = repr(config)
    assert 'VllmConfig' in val
    assert 'inductor_passes' in val


28
29
30
31
32
@dataclass
class _TestConfigFields:
    a: int
    b: dict = field(default_factory=dict)
    c: str = "default"
33
34


35
def test_get_field():
36
    with pytest.raises(ValueError):
37
        get_field(_TestConfigFields, "a")
38

39
    b = get_field(_TestConfigFields, "b")
40
41
42
43
    assert isinstance(b, Field)
    assert b.default is MISSING
    assert b.default_factory is dict

44
    c = get_field(_TestConfigFields, "c")
45
46
47
48
49
    assert isinstance(c, Field)
    assert c.default == "default"
    assert c.default_factory is MISSING


50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
@dataclass
class _TestNestedConfig:
    a: _TestConfigFields = field(
        default_factory=lambda: _TestConfigFields(a=0))


def test_update_config():
    # Simple update
    config1 = _TestConfigFields(a=0)
    new_config1 = update_config(config1, {"a": 42})
    assert new_config1.a == 42
    # Nonexistent field
    with pytest.raises(AssertionError):
        new_config1 = update_config(config1, {"nonexistent": 1})
    # Nested update with dataclass
    config2 = _TestNestedConfig()
    new_inner_config = _TestConfigFields(a=1, c="new_value")
    new_config2 = update_config(config2, {"a": new_inner_config})
    assert new_config2.a == new_inner_config
    # Nested update with dict
    config3 = _TestNestedConfig()
    new_config3 = update_config(config3, {"a": {"c": "new_value"}})
    assert new_config3.a.c == "new_value"
    # Nested update with invalid type
    with pytest.raises(AssertionError):
        new_config3 = update_config(config3, {"a": "new_value"})


78
# Can remove once --task option is fully deprecated
79
@pytest.mark.parametrize(
80
81
    ("model_id", "expected_runner_type", "expected_convert_type",
     "expected_task"),
82
    [
83
84
85
86
87
88
89
        ("distilbert/distilgpt2", "generate", "none", "generate"),
        ("intfloat/multilingual-e5-small", "pooling", "none", "embed"),
        ("jason9693/Qwen2.5-1.5B-apeach", "pooling", "classify", "classify"),
        ("cross-encoder/ms-marco-MiniLM-L-6-v2", "pooling", "none",
         "classify"),
        ("Qwen/Qwen2.5-Math-RM-72B", "pooling", "none", "reward"),
        ("openai/whisper-small", "generate", "none", "transcription"),
90
91
    ],
)
92
93
94
def test_auto_task(model_id, expected_runner_type, expected_convert_type,
                   expected_task):
    config = ModelConfig(model_id, task="auto")
95
96

    assert config.runner_type == expected_runner_type
97
98
    assert config.convert_type == expected_convert_type
    assert expected_task in config.supported_tasks
99

100

101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
# Can remove once --task option is fully deprecated
@pytest.mark.parametrize(
    ("model_id", "expected_runner_type", "expected_convert_type",
     "expected_task"),
    [
        ("distilbert/distilgpt2", "pooling", "embed", "embed"),
        ("intfloat/multilingual-e5-small", "pooling", "embed", "embed"),
        ("jason9693/Qwen2.5-1.5B-apeach", "pooling", "classify", "classify"),
        ("cross-encoder/ms-marco-MiniLM-L-6-v2", "pooling", "classify",
         "classify"),
        ("Qwen/Qwen2.5-Math-RM-72B", "pooling", "embed", "embed"),
        ("openai/whisper-small", "pooling", "embed", "embed"),
    ],
)
def test_score_task(model_id, expected_runner_type, expected_convert_type,
                    expected_task):
    config = ModelConfig(model_id, task="score")
118

119
120
121
122
123
124
    assert config.runner_type == expected_runner_type
    assert config.convert_type == expected_convert_type
    assert expected_task in config.supported_tasks


# Can remove once --task option is fully deprecated
125
@pytest.mark.parametrize(
126
127
    ("model_id", "expected_runner_type", "expected_convert_type",
     "expected_task"),
128
    [
129
        ("openai/whisper-small", "generate", "none", "transcription"),
130
131
    ],
)
132
133
134
def test_transcription_task(model_id, expected_runner_type,
                            expected_convert_type, expected_task):
    config = ModelConfig(model_id, task="transcription")
135

136
    assert config.runner_type == expected_runner_type
137
138
    assert config.convert_type == expected_convert_type
    assert expected_task in config.supported_tasks
139
140


141
142
143
144
145
146
147
148
149
150
151
152
153
@pytest.mark.parametrize(
    ("model_id", "expected_runner_type", "expected_convert_type"),
    [
        ("distilbert/distilgpt2", "generate", "none"),
        ("intfloat/multilingual-e5-small", "pooling", "none"),
        ("jason9693/Qwen2.5-1.5B-apeach", "pooling", "classify"),
        ("cross-encoder/ms-marco-MiniLM-L-6-v2", "pooling", "none"),
        ("Qwen/Qwen2.5-Math-RM-72B", "pooling", "none"),
        ("openai/whisper-small", "generate", "none"),
    ],
)
def test_auto_runner(model_id, expected_runner_type, expected_convert_type):
    config = ModelConfig(model_id, runner="auto")
154
155

    assert config.runner_type == expected_runner_type
156
    assert config.convert_type == expected_convert_type
157
158
159


@pytest.mark.parametrize(
160
    ("model_id", "expected_runner_type", "expected_convert_type"),
161
    [
162
163
164
165
166
167
        ("distilbert/distilgpt2", "pooling", "embed"),
        ("intfloat/multilingual-e5-small", "pooling", "none"),
        ("jason9693/Qwen2.5-1.5B-apeach", "pooling", "classify"),
        ("cross-encoder/ms-marco-MiniLM-L-6-v2", "pooling", "none"),
        ("Qwen/Qwen2.5-Math-RM-72B", "pooling", "none"),
        ("openai/whisper-small", "pooling", "embed"),
168
169
    ],
)
170
171
def test_pooling_runner(model_id, expected_runner_type, expected_convert_type):
    config = ModelConfig(model_id, runner="pooling")
172
173

    assert config.runner_type == expected_runner_type
174
    assert config.convert_type == expected_convert_type
175
176


177
178
179
180
181
182
183
184
185
186
187
@pytest.mark.parametrize(
    ("model_id", "expected_runner_type", "expected_convert_type"),
    [
        ("Qwen/Qwen2.5-1.5B-Instruct", "draft", "none"),
    ],
)
def test_draft_runner(model_id, expected_runner_type, expected_convert_type):
    config = ModelConfig(model_id, runner="draft")

    assert config.runner_type == expected_runner_type
    assert config.convert_type == expected_convert_type
188
189


190
191
192
193
194
195
196
197
198
199
MODEL_IDS_EXPECTED = [
    ("Qwen/Qwen1.5-7B", 32768),
    ("mistralai/Mistral-7B-v0.1", 4096),
    ("mistralai/Mistral-7B-Instruct-v0.2", 32768),
]


@pytest.mark.parametrize("model_id_expected", MODEL_IDS_EXPECTED)
def test_disable_sliding_window(model_id_expected):
    model_id, expected = model_id_expected
200
    model_config = ModelConfig(model_id, disable_sliding_window=True)
201
202
    assert model_config.max_model_len == expected

203

204
205
206
207
@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Xformers backend is not supported on ROCm.")
def test_get_pooling_config():
    model_id = "sentence-transformers/all-MiniLM-L12-v2"
208
    model_config = ModelConfig(model_id)
209

210
211
212
    assert model_config.pooler_config is not None
    assert model_config.pooler_config.normalize
    assert model_config.pooler_config.pooling_type == PoolingType.MEAN.name
213
214
215
216
217
218


@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Xformers backend is not supported on ROCm.")
def test_get_pooling_config_from_args():
    model_id = "sentence-transformers/all-MiniLM-L12-v2"
219
220
    pooler_config = PoolerConfig(pooling_type="CLS", normalize=True)
    model_config = ModelConfig(model_id, pooler_config=pooler_config)
221

222
    assert asdict(model_config.pooler_config) == asdict(pooler_config)
223
224


225
226
227
228
229
230
231
232
233
234
235
236
237
238
@pytest.mark.parametrize(
    ("model_id", "default_pooling_type", "pooling_type"),
    [
        ("tomaarsen/Qwen3-Reranker-0.6B-seq-cls", "LAST", "LAST"),  # LLM
        ("intfloat/e5-small", "CLS", "MEAN"),  # BertModel
        ("Qwen/Qwen2.5-Math-RM-72B", "ALL", "ALL"),  # reward
        ("Qwen/Qwen2.5-Math-PRM-7B", "STEP", "STEP")  # step reward
    ])
def test_default_pooling_type(model_id, default_pooling_type, pooling_type):
    model_config = ModelConfig(model_id)
    assert model_config._model_info.default_pooling_type == default_pooling_type
    assert model_config.pooler_config.pooling_type == pooling_type


239
240
241
@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Xformers backend is not supported on ROCm.")
def test_get_bert_tokenization_sentence_transformer_config():
242
243
    model_id = "BAAI/bge-base-en-v1.5"
    bge_model_config = ModelConfig(model_id)
244
245
246
247
248
249
250

    bert_bge_model_config = bge_model_config._get_encoder_config()

    assert bert_bge_model_config["max_seq_length"] == 512
    assert bert_bge_model_config["do_lower_case"]


251
def test_rope_customization():
252
    TEST_ROPE_SCALING = {"rope_type": "dynamic", "factor": 2.0}
253
    TEST_ROPE_THETA = 16_000_000.0
254
    LONGCHAT_ROPE_SCALING = {"rope_type": "linear", "factor": 8.0}
255

256
    llama_model_config = ModelConfig("meta-llama/Meta-Llama-3-8B-Instruct")
257
    assert getattr(llama_model_config.hf_config, "rope_scaling", None) is None
258
    assert getattr(llama_model_config.hf_config, "rope_theta", None) == 500_000
259
260
261
262
    assert llama_model_config.max_model_len == 8192

    llama_model_config = ModelConfig(
        "meta-llama/Meta-Llama-3-8B-Instruct",
263
264
265
266
        hf_overrides={
            "rope_scaling": TEST_ROPE_SCALING,
            "rope_theta": TEST_ROPE_THETA,
        },
267
268
269
    )
    assert getattr(llama_model_config.hf_config, "rope_scaling",
                   None) == TEST_ROPE_SCALING
270
271
    assert getattr(llama_model_config.hf_config, "rope_theta",
                   None) == TEST_ROPE_THETA
272
273
    assert llama_model_config.max_model_len == 16384

274
    longchat_model_config = ModelConfig("lmsys/longchat-13b-16k")
275
276
277
278
279
280
281
282
    # Check if LONGCHAT_ROPE_SCALING entries are in longchat_model_config
    assert all(
        longchat_model_config.hf_config.rope_scaling.get(key) == value
        for key, value in LONGCHAT_ROPE_SCALING.items())
    assert longchat_model_config.max_model_len == 16384

    longchat_model_config = ModelConfig(
        "lmsys/longchat-13b-16k",
283
284
285
        hf_overrides={
            "rope_scaling": TEST_ROPE_SCALING,
        },
286
287
288
289
    )
    assert getattr(longchat_model_config.hf_config, "rope_scaling",
                   None) == TEST_ROPE_SCALING
    assert longchat_model_config.max_model_len == 4096
290
291


292
293
@pytest.mark.skipif(current_platform.is_rocm(),
                    reason="Encoder Decoder models not supported on ROCm.")
294
295
@pytest.mark.parametrize(("model_id", "is_encoder_decoder"), [
    ("facebook/opt-125m", False),
296
    ("openai/whisper-tiny", True),
297
    ("meta-llama/Llama-3.2-1B-Instruct", False),
298
299
])
def test_is_encoder_decoder(model_id, is_encoder_decoder):
300
    config = ModelConfig(model_id)
301
302
303
304
305
306
307
308
309

    assert config.is_encoder_decoder == is_encoder_decoder


@pytest.mark.parametrize(("model_id", "uses_mrope"), [
    ("facebook/opt-125m", False),
    ("Qwen/Qwen2-VL-2B-Instruct", True),
])
def test_uses_mrope(model_id, uses_mrope):
310
    config = ModelConfig(model_id)
311
312

    assert config.uses_mrope == uses_mrope
313
314
315
316
317


def test_generation_config_loading():
    model_id = "Qwen/Qwen2.5-1.5B-Instruct"

318
    # When set generation_config to "vllm", the default generation config
319
    # will not be loaded.
320
    model_config = ModelConfig(model_id, generation_config="vllm")
321
322
323
324
    assert model_config.get_diff_sampling_param() == {}

    # When set generation_config to "auto", the default generation config
    # should be loaded.
325
    model_config = ModelConfig(model_id, generation_config="auto")
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348

    correct_generation_config = {
        "repetition_penalty": 1.1,
        "temperature": 0.7,
        "top_p": 0.8,
        "top_k": 20,
    }

    assert model_config.get_diff_sampling_param() == correct_generation_config

    # The generation config could be overridden by the user.
    override_generation_config = {"temperature": 0.5, "top_k": 5}

    model_config = ModelConfig(
        model_id,
        generation_config="auto",
        override_generation_config=override_generation_config)

    override_result = correct_generation_config.copy()
    override_result.update(override_generation_config)

    assert model_config.get_diff_sampling_param() == override_result

349
    # When generation_config is set to "vllm" and override_generation_config
350
351
352
    # is set, the override_generation_config should be used directly.
    model_config = ModelConfig(
        model_id,
353
        generation_config="vllm",
354
355
356
        override_generation_config=override_generation_config)

    assert model_config.get_diff_sampling_param() == override_generation_config
357
358
359
360
361
362
363
364
365
366
367
368
369


@pytest.mark.parametrize("pt_load_map_location", [
    "cuda",
    {
        "": "cuda"
    },
])
def test_load_config_pt_load_map_location(pt_load_map_location):
    load_config = LoadConfig(pt_load_map_location=pt_load_map_location)
    config = VllmConfig(load_config=load_config)

    assert config.load_config.pt_load_map_location == pt_load_map_location
370
371
372
373
374
375
376


@pytest.mark.parametrize(
    ("model_id", "max_model_len", "expected_max_len", "should_raise"), [
        ("BAAI/bge-reranker-base", None, 512, False),
        ("BAAI/bge-reranker-base", 256, 256, False),
        ("BAAI/bge-reranker-base", 513, 512, True),
377
378
        ("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", None, 131072, False),
        ("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", 131073, 131072, True),
379
380
381
382
    ])
def test_get_and_verify_max_len(model_id, max_model_len, expected_max_len,
                                should_raise):
    """Test get_and_verify_max_len with different configurations."""
383
    model_config = ModelConfig(model_id)
384
385
386
387
388
389
390

    if should_raise:
        with pytest.raises(ValueError):
            model_config.get_and_verify_max_len(max_model_len)
    else:
        actual_max_len = model_config.get_and_verify_max_len(max_model_len)
        assert actual_max_len == expected_max_len